Overview

Dataset statistics

Number of variables29
Number of observations124663
Missing cells12363
Missing cells (%)0.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory13.7 MiB
Average record size in memory115.4 B

Variable types

Numeric11
DateTime1
Categorical17

Warnings

BARRIO has a high cardinality: 408 distinct values High cardinality
UNIDAD_ESPACIAL has a high cardinality: 278 distinct values High cardinality
TIPO_CONDUCTA has a high cardinality: 111 distinct values High cardinality
CRIMEN_ID is highly correlated with AÑOHigh correlation
AÑO is highly correlated with CRIMEN_IDHigh correlation
EDAD_VICTIMA is highly correlated with GRUPO_ETARIO_VICTIMA_numHigh correlation
GRUPO_ETARIO_VICTIMA_num is highly correlated with EDAD_VICTIMAHigh correlation
CRIMEN_ID is highly correlated with AÑOHigh correlation
AÑO is highly correlated with CRIMEN_IDHigh correlation
EDAD_VICTIMA is highly correlated with GRUPO_ETARIO_VICTIMA_numHigh correlation
GRUPO_ETARIO_VICTIMA_num is highly correlated with EDAD_VICTIMAHigh correlation
CRIMEN_ID is highly correlated with AÑOHigh correlation
AÑO is highly correlated with CRIMEN_IDHigh correlation
DIA_SEMANA is highly correlated with DIA_SEMANA_numHigh correlation
AÑO is highly correlated with CRIMEN_IDHigh correlation
TIPO_DELITO is highly correlated with EDAD_VICTIMA and 10 other fieldsHigh correlation
ZONA is highly correlated with COMUNAHigh correlation
EDAD_VICTIMA is highly correlated with TIPO_DELITO and 4 other fieldsHigh correlation
TIPO_ARMA is highly correlated with TIPO_DELITO and 4 other fieldsHigh correlation
TIPO_LESION is highly correlated with TIPO_DELITO and 3 other fieldsHigh correlation
DISTANCIA_ESTACION_POLICIA_CERCANA is highly correlated with LONGITUD and 1 other fieldsHigh correlation
GENERO_VICTIMA is highly correlated with TIPO_DELITO and 5 other fieldsHigh correlation
MEDIO_TRANSPORTE_VICTIMA is highly correlated with TIPO_DELITO and 3 other fieldsHigh correlation
LONGITUD is highly correlated with DISTANCIA_ESTACION_POLICIA_CERCANA and 1 other fieldsHigh correlation
LATITUD is highly correlated with DISTANCIA_ESTACION_POLICIA_CERCANA and 1 other fieldsHigh correlation
TIPO_DELITO_ARTICULO is highly correlated with TIPO_DELITO and 8 other fieldsHigh correlation
MEDIO_TRANSPORTE_VICTIMARIO is highly correlated with TIPO_DELITO and 3 other fieldsHigh correlation
CRIMEN_ID is highly correlated with AÑOHigh correlation
COMUNA is highly correlated with ZONA and 2 other fieldsHigh correlation
DIA_SEMANA_num is highly correlated with DIA_SEMANAHigh correlation
MES_num is highly correlated with MESHigh correlation
GRUPO_ETARIO_VICTIMA_num is highly correlated with TIPO_DELITO and 5 other fieldsHigh correlation
ESTADO_CIVIL_VICTIMA is highly correlated with TIPO_DELITO and 4 other fieldsHigh correlation
GRUPO_ETARIO_VICTIMA is highly correlated with TIPO_DELITO and 5 other fieldsHigh correlation
COMUNA_num is highly correlated with COMUNA and 1 other fieldsHigh correlation
ESTACION_POLICIA_CERCANA is highly correlated with TIPO_DELITO and 4 other fieldsHigh correlation
MES is highly correlated with MES_numHigh correlation
TIPO_DELITO_ARTICULO is highly correlated with TIPO_DELITO and 1 other fieldsHigh correlation
ESTACION_POLICIA_CERCANA is highly correlated with COMUNA and 1 other fieldsHigh correlation
TIPO_DELITO is highly correlated with TIPO_DELITO_ARTICULO and 2 other fieldsHigh correlation
ZONA is highly correlated with COMUNAHigh correlation
COMUNA is highly correlated with ESTACION_POLICIA_CERCANA and 1 other fieldsHigh correlation
TIPO_LESION is highly correlated with TIPO_DELITO_ARTICULO and 2 other fieldsHigh correlation
GENERO_VICTIMA is highly correlated with TIPO_DELITO and 2 other fieldsHigh correlation
ESTADO_CIVIL_VICTIMA is highly correlated with GENERO_VICTIMAHigh correlation
GRUPO_ETARIO_VICTIMA is highly correlated with GENERO_VICTIMAHigh correlation
LATITUD has 4121 (3.3%) missing values Missing
LONGITUD has 4121 (3.3%) missing values Missing
DISTANCIA_ESTACION_POLICIA_CERCANA has 4121 (3.3%) missing values Missing
DISTANCIA_ESTACION_POLICIA_CERCANA is highly skewed (γ1 = 214.9790344) Skewed
CRIMEN_ID is uniformly distributed Uniform
CRIMEN_ID has unique values Unique
GRUPO_ETARIO_VICTIMA_num has 8152 (6.5%) zeros Zeros

Reproduction

Analysis started2021-09-03 15:44:03.529631
Analysis finished2021-09-03 16:08:22.350595
Duration24 minutes and 18.82 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

CRIMEN_ID
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct124663
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62332
Minimum1
Maximum124663
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size974.1 KiB
2021-09-03T11:08:22.501158image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6234.1
Q131166.5
median62332
Q393497.5
95-th percentile118429.9
Maximum124663
Range124662
Interquartile range (IQR)62331

Descriptive statistics

Standard deviation35987.25264
Coefficient of variation (CV)0.5773479536
Kurtosis-1.2
Mean62332
Median Absolute Deviation (MAD)31166
Skewness-2.203112978 × 10-17
Sum7770494116
Variance1295082353
MonotonicityNot monotonic
2021-09-03T11:08:22.724993image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11
 
< 0.1%
830731
 
< 0.1%
831021
 
< 0.1%
831011
 
< 0.1%
831001
 
< 0.1%
830991
 
< 0.1%
830981
 
< 0.1%
830971
 
< 0.1%
830961
 
< 0.1%
830951
 
< 0.1%
Other values (124653)124653
> 99.9%
ValueCountFrequency (%)
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
101
< 0.1%
ValueCountFrequency (%)
1246631
< 0.1%
1246621
< 0.1%
1246611
< 0.1%
1246601
< 0.1%
1246591
< 0.1%
1246581
< 0.1%
1246571
< 0.1%
1246561
< 0.1%
1246551
< 0.1%
1246541
< 0.1%

FECHA
Date

Distinct4072
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size974.1 KiB
Minimum2010-01-01 00:00:00
Maximum2021-02-28 00:00:00
2021-09-03T11:08:22.944803image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:08:23.154174image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

AÑO
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2015.287094
Minimum2010
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size974.1 KiB
2021-09-03T11:08:23.337502image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum2010
5-th percentile2010
Q12013
median2016
Q32018
95-th percentile2020
Maximum2021
Range11
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.102117373
Coefficient of variation (CV)0.001539293028
Kurtosis-1.14941778
Mean2015.287094
Median Absolute Deviation (MAD)3
Skewness-0.1054762854
Sum251231735
Variance9.623132194
MonotonicityNot monotonic
2021-09-03T11:08:23.496262image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
201613600
10.9%
201813489
10.8%
201912509
10.0%
201712298
9.9%
201210912
8.8%
201510825
8.7%
201310759
8.6%
201110363
8.3%
201410214
8.2%
20209552
7.7%
Other values (2)10142
8.1%
ValueCountFrequency (%)
20108691
7.0%
201110363
8.3%
201210912
8.8%
201310759
8.6%
201410214
8.2%
201510825
8.7%
201613600
10.9%
201712298
9.9%
201813489
10.8%
201912509
10.0%
ValueCountFrequency (%)
20211451
 
1.2%
20209552
7.7%
201912509
10.0%
201813489
10.8%
201712298
9.9%
201613600
10.9%
201510825
8.7%
201410214
8.2%
201310759
8.6%
201210912
8.8%

MES
Categorical

HIGH CORRELATION

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size122.3 KiB
ENERO
11275 
DICIEMBRE
11234 
OCTUBRE
11039 
FEBRERO
10996 
SEPTIEMBRE
10524 
Other values (7)
69595 

Length

Max length10
Median length6
Mean length6.460449372
Min length4

Characters and Unicode

Total characters805379
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowENERO
2nd rowENERO
3rd rowENERO
4th rowENERO
5th rowENERO

Common Values

ValueCountFrequency (%)
ENERO11275
9.0%
DICIEMBRE11234
9.0%
OCTUBRE11039
8.9%
FEBRERO10996
8.8%
SEPTIEMBRE10524
8.4%
AGOSTO10293
8.3%
JULIO10110
8.1%
NOVIEMBRE10045
8.1%
MAYO10035
8.0%
MARZO9933
8.0%
Other values (2)19179
15.4%

Length

2021-09-03T11:08:23.995136image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
enero11275
9.0%
diciembre11234
9.0%
octubre11039
8.9%
febrero10996
8.8%
septiembre10524
8.4%
agosto10293
8.3%
julio10110
8.1%
noviembre10045
8.1%
mayo10035
8.0%
marzo9933
8.0%
Other values (2)19179
15.4%

Most occurring characters

ValueCountFrequency (%)
E129711
16.1%
O103629
12.9%
R95611
11.9%
I72326
9.0%
B63407
7.9%
M51771
 
6.4%
A39830
 
4.9%
T31856
 
4.0%
N30930
 
3.8%
U30759
 
3.8%
Other values (11)155549
19.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter805379
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E129711
16.1%
O103629
12.9%
R95611
11.9%
I72326
9.0%
B63407
7.9%
M51771
 
6.4%
A39830
 
4.9%
T31856
 
4.0%
N30930
 
3.8%
U30759
 
3.8%
Other values (11)155549
19.3%

Most occurring scripts

ValueCountFrequency (%)
Latin805379
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E129711
16.1%
O103629
12.9%
R95611
11.9%
I72326
9.0%
B63407
7.9%
M51771
 
6.4%
A39830
 
4.9%
T31856
 
4.0%
N30930
 
3.8%
U30759
 
3.8%
Other values (11)155549
19.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII805379
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E129711
16.1%
O103629
12.9%
R95611
11.9%
I72326
9.0%
B63407
7.9%
M51771
 
6.4%
A39830
 
4.9%
T31856
 
4.0%
N30930
 
3.8%
U30759
 
3.8%
Other values (11)155549
19.3%

MES_num
Real number (ℝ≥0)

HIGH CORRELATION

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.519175698
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size974.1 KiB
2021-09-03T11:08:24.126815image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.514841219
Coefficient of variation (CV)0.53915424
Kurtosis-1.251517787
Mean6.519175698
Median Absolute Deviation (MAD)3
Skewness-0.02194759484
Sum812700
Variance12.35410879
MonotonicityNot monotonic
2021-09-03T11:08:24.252478image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
111275
9.0%
1211234
9.0%
1011039
8.9%
210996
8.8%
910524
8.4%
810293
8.3%
710110
8.1%
1110045
8.1%
510035
8.0%
39933
8.0%
Other values (2)19179
15.4%
ValueCountFrequency (%)
111275
9.0%
210996
8.8%
39933
8.0%
49569
7.7%
510035
8.0%
69610
7.7%
710110
8.1%
810293
8.3%
910524
8.4%
1011039
8.9%
ValueCountFrequency (%)
1211234
9.0%
1110045
8.1%
1011039
8.9%
910524
8.4%
810293
8.3%
710110
8.1%
69610
7.7%
510035
8.0%
49569
7.7%
39933
8.0%

DIA
Real number (ℝ≥0)

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.45598935
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size974.1 KiB
2021-09-03T11:08:24.431437image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median15
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.825933381
Coefficient of variation (CV)0.571036456
Kurtosis-1.19162181
Mean15.45598935
Median Absolute Deviation (MAD)8
Skewness0.03446042033
Sum1926790
Variance77.89710004
MonotonicityNot monotonic
2021-09-03T11:08:24.785960image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
14813
 
3.9%
104346
 
3.5%
24297
 
3.4%
54231
 
3.4%
44194
 
3.4%
34188
 
3.4%
94184
 
3.4%
164166
 
3.3%
234153
 
3.3%
224143
 
3.3%
Other values (21)81948
65.7%
ValueCountFrequency (%)
14813
3.9%
24297
3.4%
34188
3.4%
44194
3.4%
54231
3.4%
64047
3.2%
74116
3.3%
84114
3.3%
94184
3.4%
104346
3.5%
ValueCountFrequency (%)
312306
1.8%
303667
2.9%
293448
2.8%
283980
3.2%
274034
3.2%
263827
3.1%
253963
3.2%
243983
3.2%
234153
3.3%
224143
3.3%

DIA_SEMANA
Categorical

HIGH CORRELATION

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size122.2 KiB
SÁBADO
20038 
VIERNES
17866 
MIÉRCOLES
17617 
MARTES
17460 
DOMINGO
17418 
Other values (2)
34264 

Length

Max length9
Median length6
Mean length6.568637045
Min length5

Characters and Unicode

Total characters818866
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVIERNES
2nd rowVIERNES
3rd rowVIERNES
4th rowVIERNES
5th rowVIERNES

Common Values

ValueCountFrequency (%)
SÁBADO20038
16.1%
VIERNES17866
14.3%
MIÉRCOLES17617
14.1%
MARTES17460
14.0%
DOMINGO17418
14.0%
LUNES17247
13.8%
JUEVES17017
13.7%

Length

2021-09-03T11:08:25.197685image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-03T11:08:25.339305image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
sábado20038
16.1%
viernes17866
14.3%
miércoles17617
14.1%
martes17460
14.0%
domingo17418
14.0%
lunes17247
13.8%
jueves17017
13.7%

Most occurring characters

ValueCountFrequency (%)
E122090
14.9%
S107245
13.1%
O72491
 
8.9%
R52943
 
6.5%
I52901
 
6.5%
N52531
 
6.4%
M52495
 
6.4%
A37498
 
4.6%
D37456
 
4.6%
V34883
 
4.3%
Other values (9)196333
24.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter818866
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E122090
14.9%
S107245
13.1%
O72491
 
8.9%
R52943
 
6.5%
I52901
 
6.5%
N52531
 
6.4%
M52495
 
6.4%
A37498
 
4.6%
D37456
 
4.6%
V34883
 
4.3%
Other values (9)196333
24.0%

Most occurring scripts

ValueCountFrequency (%)
Latin818866
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E122090
14.9%
S107245
13.1%
O72491
 
8.9%
R52943
 
6.5%
I52901
 
6.5%
N52531
 
6.4%
M52495
 
6.4%
A37498
 
4.6%
D37456
 
4.6%
V34883
 
4.3%
Other values (9)196333
24.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII781211
95.4%
Latin 1 Sup37655
 
4.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E122090
15.6%
S107245
13.7%
O72491
9.3%
R52943
 
6.8%
I52901
 
6.8%
N52531
 
6.7%
M52495
 
6.7%
A37498
 
4.8%
D37456
 
4.8%
V34883
 
4.5%
Other values (7)158678
20.3%
Latin 1 Sup
ValueCountFrequency (%)
Á20038
53.2%
É17617
46.8%

DIA_SEMANA_num
Real number (ℝ≥0)

HIGH CORRELATION

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.047471984
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size974.1 KiB
2021-09-03T11:08:25.481950image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.997051983
Coefficient of variation (CV)0.4934072406
Kurtosis-1.262496204
Mean4.047471984
Median Absolute Deviation (MAD)2
Skewness-0.04565478858
Sum504570
Variance3.988216624
MonotonicityNot monotonic
2021-09-03T11:08:25.619982image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
620038
16.1%
517866
14.3%
317617
14.1%
217460
14.0%
717418
14.0%
117247
13.8%
417017
13.7%
ValueCountFrequency (%)
117247
13.8%
217460
14.0%
317617
14.1%
417017
13.7%
517866
14.3%
620038
16.1%
717418
14.0%
ValueCountFrequency (%)
717418
14.0%
620038
16.1%
517866
14.3%
417017
13.7%
317617
14.1%
217460
14.0%
117247
13.8%

LATITUD
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct56276
Distinct (%)46.7%
Missing4121
Missing (%)3.3%
Infinite0
Infinite (%)0.0%
Mean7.124001014
Minimum4.5901267
Maximum7.239192
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size974.1 KiB
2021-09-03T11:08:25.817493image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum4.5901267
5-th percentile7.0869384
Q17.1089894
median7.1214947
Q37.138172825
95-th percentile7.1705574
Maximum7.239192
Range2.6490653
Interquartile range (IQR)0.029183425

Descriptive statistics

Standard deviation0.02448501873
Coefficient of variation (CV)0.003436975751
Kurtosis951.039271
Mean7.124001014
Median Absolute Deviation (MAD)0.0147122
Skewness-8.838809403
Sum858741.3302
Variance0.0005995161424
MonotonicityNot monotonic
2021-09-03T11:08:26.039703image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.17804571799
 
1.4%
7.17055741375
 
1.1%
7.1443711163
 
0.9%
7.16462391073
 
0.9%
7.12064541004
 
0.8%
7.1389313842
 
0.7%
7.1217487804
 
0.6%
7.1345208768
 
0.6%
7.1481037761
 
0.6%
7.1218431703
 
0.6%
Other values (56266)110250
88.4%
(Missing)4121
 
3.3%
ValueCountFrequency (%)
4.59012671
< 0.1%
7.00261151
< 0.1%
7.05836261
< 0.1%
7.0607321
< 0.1%
7.06080661
< 0.1%
7.06531261
< 0.1%
7.06852261
< 0.1%
7.07089111
< 0.1%
7.07096411
< 0.1%
7.07108191
< 0.1%
ValueCountFrequency (%)
7.2391921
 
< 0.1%
7.22820321
 
< 0.1%
7.228111
 
< 0.1%
7.2128211
 
< 0.1%
7.21233926
 
< 0.1%
7.21098431
 
< 0.1%
7.21074131
 
< 0.1%
7.21032521
 
< 0.1%
7.209980743
< 0.1%
7.20923111
 
< 0.1%

LONGITUD
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct49226
Distinct (%)40.8%
Missing4121
Missing (%)3.3%
Infinite0
Infinite (%)0.0%
Mean-73.12425775
Minimum-74.1939044
Maximum-73.0546927
Zeros0
Zeros (%)0.0%
Negative120542
Negative (%)96.7%
Memory size974.1 KiB
2021-09-03T11:08:26.272888image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-74.1939044
5-th percentile-73.14082138
Q1-73.1317162
median-73.12468825
Q3-73.1160843
95-th percentile-73.10690871
Maximum-73.0546927
Range1.1392117
Interquartile range (IQR)0.0156319

Descriptive statistics

Standard deviation0.01151763896
Coefficient of variation (CV)-0.0001575077726
Kurtosis617.3517124
Mean-73.12425775
Median Absolute Deviation (MAD)0.00781565
Skewness-6.816839956
Sum-8814544.277
Variance0.0001326560073
MonotonicityNot monotonic
2021-09-03T11:08:26.507308image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-73.13022441799
 
1.4%
-73.1351081375
 
1.1%
-73.1280851163
 
0.9%
-73.13914051081
 
0.9%
-73.126051004
 
0.8%
-73.1202363848
 
0.7%
-73.118304805
 
0.6%
-73.11588768
 
0.6%
-73.1263899761
 
0.6%
-73.139995703
 
0.6%
Other values (49216)110235
88.4%
(Missing)4121
 
3.3%
ValueCountFrequency (%)
-74.19390441
< 0.1%
-73.17612561
< 0.1%
-73.17205721
< 0.1%
-73.17194371
< 0.1%
-73.1719071
< 0.1%
-73.17189621
< 0.1%
-73.17188491
< 0.1%
-73.17185191
< 0.1%
-73.1718421
< 0.1%
-73.17179912
< 0.1%
ValueCountFrequency (%)
-73.05469271
< 0.1%
-73.05486451
< 0.1%
-73.05776851
< 0.1%
-73.05813391
< 0.1%
-73.06369271
< 0.1%
-73.06377851
< 0.1%
-73.06382151
< 0.1%
-73.064451
< 0.1%
-73.07057921
< 0.1%
-73.07290621
< 0.1%

ZONA
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size122.0 KiB
URBANA
123563 
RURAL
 
1058
OTRA
 
42

Length

Max length6
Median length6
Mean length5.990839303
Min length4

Characters and Unicode

Total characters746836
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowURBANA
2nd rowURBANA
3rd rowURBANA
4th rowURBANA
5th rowURBANA

Common Values

ValueCountFrequency (%)
URBANA123563
99.1%
RURAL1058
 
0.8%
OTRA42
 
< 0.1%

Length

2021-09-03T11:08:26.938837image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-03T11:08:27.109383image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
urbana123563
99.1%
rural1058
 
0.8%
otra42
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
A248226
33.2%
R125721
16.8%
U124621
16.7%
B123563
16.5%
N123563
16.5%
L1058
 
0.1%
O42
 
< 0.1%
T42
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter746836
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A248226
33.2%
R125721
16.8%
U124621
16.7%
B123563
16.5%
N123563
16.5%
L1058
 
0.1%
O42
 
< 0.1%
T42
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin746836
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A248226
33.2%
R125721
16.8%
U124621
16.7%
B123563
16.5%
N123563
16.5%
L1058
 
0.1%
O42
 
< 0.1%
T42
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII746836
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A248226
33.2%
R125721
16.8%
U124621
16.7%
B123563
16.5%
N123563
16.5%
L1058
 
0.1%
O42
 
< 0.1%
T42
 
< 0.1%

COMUNA
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size122.6 KiB
SAN FRANCISCO
14717 
CENTRO
12665 
ORIENTAL
12265 
NORTE
11797 
CABECERA DEL LLANO
11747 
Other values (16)
61472 

Length

Max length19
Median length13
Mean length11.3374618
Min length4

Characters and Unicode

Total characters1413362
Distinct characters25
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row MORRORICO
2nd row GARCÍA ROVIRA
3rd row GARCÍA ROVIRA
4th row SAN FRANCISCO
5th row OCCIDENTAL

Common Values

ValueCountFrequency (%)
SAN FRANCISCO14717
11.8%
CENTRO12665
10.2%
ORIENTAL12265
9.8%
NORTE11797
9.5%
CABECERA DEL LLANO11747
9.4%
LA CONCORDIA9209
 
7.4%
GARCÍA ROVIRA8473
 
6.8%
OCCIDENTAL7194
 
5.8%
PROVENZA6068
 
4.9%
LA PEDREGOSA4770
 
3.8%
Other values (11)25758
20.7%

Length

2021-09-03T11:08:27.524276image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
la16965
 
8.4%
oriental16623
 
8.2%
del14731
 
7.3%
san14717
 
7.3%
francisco14717
 
7.3%
centro12665
 
6.2%
norte11797
 
5.8%
llano11747
 
5.8%
cabecera11747
 
5.8%
concordia9209
 
4.5%
Other values (19)67976
33.5%

Most occurring characters

ValueCountFrequency (%)
201859
14.3%
A162908
11.5%
R135883
9.6%
O134114
9.5%
C126261
8.9%
E116549
8.2%
N113351
8.0%
L84977
6.0%
I74351
 
5.3%
T56496
 
4.0%
Other values (15)206613
14.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter1210513
85.6%
Space Separator201859
 
14.3%
Decimal Number990
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A162908
13.5%
R135883
11.2%
O134114
11.1%
C126261
10.4%
E116549
9.6%
N113351
9.4%
L84977
7.0%
I74351
6.1%
T56496
 
4.7%
S48580
 
4.0%
Other values (11)157043
13.0%
Decimal Number
ValueCountFrequency (%)
1551
55.7%
3362
36.6%
277
 
7.8%
Space Separator
ValueCountFrequency (%)
201859
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1210513
85.6%
Common202849
 
14.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
A162908
13.5%
R135883
11.2%
O134114
11.1%
C126261
10.4%
E116549
9.6%
N113351
9.4%
L84977
7.0%
I74351
6.1%
T56496
 
4.7%
S48580
 
4.0%
Other values (11)157043
13.0%
Common
ValueCountFrequency (%)
201859
99.5%
1551
 
0.3%
3362
 
0.2%
277
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1404889
99.4%
Latin 1 Sup8473
 
0.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
201859
14.4%
A162908
11.6%
R135883
9.7%
O134114
9.5%
C126261
9.0%
E116549
8.3%
N113351
8.1%
L84977
6.0%
I74351
 
5.3%
T56496
 
4.0%
Other values (14)198140
14.1%
Latin 1 Sup
ValueCountFrequency (%)
Í8473
100.0%

COMUNA_num
Real number (ℝ≥0)

HIGH CORRELATION

Distinct18
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.303634599
Minimum0
Maximum17
Zeros1035
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size974.1 KiB
2021-09-03T11:08:27.822093image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median8
Q313
95-th percentile16
Maximum17
Range17
Interquartile range (IQR)10

Descriptive statistics

Standard deviation5.059222217
Coefficient of variation (CV)0.6092780404
Kurtosis-1.396679262
Mean8.303634599
Median Absolute Deviation (MAD)5
Skewness0.05122617179
Sum1035156
Variance25.59572944
MonotonicityNot monotonic
2021-09-03T11:08:28.094500image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
314717
11.8%
1512665
10.2%
1312265
9.8%
111797
9.5%
1211747
9.4%
69209
 
7.4%
58473
 
6.8%
47194
 
5.8%
106068
 
4.9%
94770
 
3.8%
Other values (8)25758
20.7%
ValueCountFrequency (%)
01035
 
0.8%
111797
9.5%
24358
 
3.5%
314717
11.8%
47194
5.8%
58473
6.8%
69209
7.4%
72986
 
2.4%
83221
 
2.6%
94770
 
3.8%
ValueCountFrequency (%)
173961
 
3.2%
162984
 
2.4%
1512665
10.2%
143003
 
2.4%
1312265
9.8%
1211747
9.4%
114210
 
3.4%
106068
4.9%
94770
 
3.8%
83221
 
2.6%

BARRIO
Categorical

HIGH CARDINALITY

Distinct408
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size263.0 KiB
CENTRO
10197 
SAN FRANCISCO
 
5452
CABECERA DEL LLANO
 
5243
LA CONCORDIA
 
4670
PROVENZA
 
3164
Other values (403)
95937 

Length

Max length32
Median length11
Mean length11.53181778
Min length4

Characters and Unicode

Total characters1437591
Distinct characters39
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique41 ?
Unique (%)< 0.1%

Sample

1st rowBUENOS AIRES
2nd rowCAMPO HERMOSO
3rd rowCAMPO HERMOSO
4th rowCOMUNEROS
5th rowGIRARDOT

Common Values

ValueCountFrequency (%)
CENTRO10197
 
8.2%
SAN FRANCISCO5452
 
4.4%
CABECERA DEL LLANO5243
 
4.2%
LA CONCORDIA4670
 
3.7%
PROVENZA3164
 
2.5%
SAN ALONSO3126
 
2.5%
CAMPO HERMOSO2624
 
2.1%
SOTOMAYOR2469
 
2.0%
GARCIA ROVIRA2469
 
2.0%
GIRARDOT2444
 
2.0%
Other values (398)82805
66.4%

Length

2021-09-03T11:08:28.814837image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
la14796
 
6.3%
san12289
 
5.2%
centro10197
 
4.3%
del9049
 
3.8%
de7233
 
3.1%
francisco5453
 
2.3%
cabecera5243
 
2.2%
llano5243
 
2.2%
concordia4670
 
2.0%
el4660
 
2.0%
Other values (402)157410
66.6%

Most occurring characters

ValueCountFrequency (%)
A203452
14.2%
O135855
9.5%
R123013
 
8.6%
111599
 
7.8%
E108597
 
7.6%
N107494
 
7.5%
L86039
 
6.0%
I85251
 
5.9%
C79663
 
5.5%
S78896
 
5.5%
Other values (29)317732
22.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter1317684
91.7%
Space Separator111599
 
7.8%
Other Punctuation6234
 
0.4%
Decimal Number831
 
0.1%
Open Punctuation612
 
< 0.1%
Close Punctuation612
 
< 0.1%
Dash Punctuation19
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A203452
15.4%
O135855
10.3%
R123013
9.3%
E108597
8.2%
N107494
8.2%
L86039
 
6.5%
I85251
 
6.5%
C79663
 
6.0%
S78896
 
6.0%
D58400
 
4.4%
Other values (17)251024
19.1%
Decimal Number
ValueCountFrequency (%)
2361
43.4%
1295
35.5%
3110
 
13.2%
033
 
4.0%
418
 
2.2%
514
 
1.7%
Other Punctuation
ValueCountFrequency (%)
.6221
99.8%
/13
 
0.2%
Space Separator
ValueCountFrequency (%)
111599
100.0%
Open Punctuation
ValueCountFrequency (%)
(612
100.0%
Close Punctuation
ValueCountFrequency (%)
)612
100.0%
Dash Punctuation
ValueCountFrequency (%)
-19
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1317684
91.7%
Common119907
 
8.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
A203452
15.4%
O135855
10.3%
R123013
9.3%
E108597
8.2%
N107494
8.2%
L86039
 
6.5%
I85251
 
6.5%
C79663
 
6.0%
S78896
 
6.0%
D58400
 
4.4%
Other values (17)251024
19.1%
Common
ValueCountFrequency (%)
111599
93.1%
.6221
 
5.2%
(612
 
0.5%
)612
 
0.5%
2361
 
0.3%
1295
 
0.2%
3110
 
0.1%
033
 
< 0.1%
-19
 
< 0.1%
418
 
< 0.1%
Other values (2)27
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1436346
99.9%
Latin 1 Sup1245
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A203452
14.2%
O135855
9.5%
R123013
 
8.6%
111599
 
7.8%
E108597
 
7.6%
N107494
 
7.5%
L86039
 
6.0%
I85251
 
5.9%
C79663
 
5.5%
S78896
 
5.5%
Other values (27)316487
22.0%
Latin 1 Sup
ValueCountFrequency (%)
Ñ1189
95.5%
Í56
 
4.5%

UNIDAD_ESPACIAL
Categorical

HIGH CARDINALITY

Distinct278
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size253.9 KiB
CENTRO
 
7525
NO REPORTA
 
5343
SAN FRANCISCO
 
4288
LA CONCORDIA
 
4154
CABECERA DEL LLANO
 
3788
Other values (273)
99565 

Length

Max length30
Median length11
Mean length11.55037982
Min length5

Characters and Unicode

Total characters1439905
Distinct characters41
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)< 0.1%

Sample

1st rowCORREGIMIENTO 1
2nd rowCENTRO
3rd rowCENTRO
4th rowVILLAS DE SAN IGNACIO
5th rowCORREGIMIENTO 1

Common Values

ValueCountFrequency (%)
CENTRO7525
 
6.0%
NO REPORTA5343
 
4.3%
SAN FRANCISCO4288
 
3.4%
LA CONCORDIA4154
 
3.3%
CABECERA DEL LLANO3788
 
3.0%
CAFE MADRID3072
 
2.5%
UNIVERSIDAD2977
 
2.4%
REAL DE MINAS2952
 
2.4%
ANTONIA SANTOS CENTRO2933
 
2.4%
GARCIA ROVIRA2787
 
2.2%
Other values (268)84844
68.1%

Length

2021-09-03T11:08:29.610326image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
la12478
 
5.4%
san11214
 
4.8%
centro10458
 
4.5%
comuna8726
 
3.7%
de6959
 
3.0%
del6046
 
2.6%
no5343
 
2.3%
reporta5343
 
2.3%
llano4371
 
1.9%
cabecera4371
 
1.9%
Other values (289)157917
67.7%

Most occurring characters

ValueCountFrequency (%)
A194377
13.5%
O141390
9.8%
R120797
 
8.4%
N117400
 
8.2%
E113870
 
7.9%
108563
 
7.5%
I93655
 
6.5%
C83111
 
5.8%
S74575
 
5.2%
L73110
 
5.1%
Other values (31)319057
22.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter1327813
92.2%
Space Separator108563
 
7.5%
Decimal Number3384
 
0.2%
Other Punctuation64
 
< 0.1%
Open Punctuation40
 
< 0.1%
Close Punctuation40
 
< 0.1%
Dash Punctuation1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A194377
14.6%
O141390
10.6%
R120797
9.1%
N117400
8.8%
E113870
8.6%
I93655
 
7.1%
C83111
 
6.3%
S74575
 
5.6%
L73110
 
5.5%
T55721
 
4.2%
Other values (20)259807
19.6%
Decimal Number
ValueCountFrequency (%)
12398
70.9%
3747
 
22.1%
2221
 
6.5%
013
 
0.4%
54
 
0.1%
71
 
< 0.1%
Space Separator
ValueCountFrequency (%)
108563
100.0%
Open Punctuation
ValueCountFrequency (%)
(40
100.0%
Close Punctuation
ValueCountFrequency (%)
)40
100.0%
Other Punctuation
ValueCountFrequency (%)
*64
100.0%
Dash Punctuation
ValueCountFrequency (%)
-1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1327813
92.2%
Common112092
 
7.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
A194377
14.6%
O141390
10.6%
R120797
9.1%
N117400
8.8%
E113870
8.6%
I93655
 
7.1%
C83111
 
6.3%
S74575
 
5.6%
L73110
 
5.5%
T55721
 
4.2%
Other values (20)259807
19.6%
Common
ValueCountFrequency (%)
108563
96.9%
12398
 
2.1%
3747
 
0.7%
2221
 
0.2%
*64
 
0.1%
(40
 
< 0.1%
)40
 
< 0.1%
013
 
< 0.1%
54
 
< 0.1%
71
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1438806
99.9%
Latin 1 Sup1099
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A194377
13.5%
O141390
9.8%
R120797
 
8.4%
N117400
 
8.2%
E113870
 
7.9%
108563
 
7.5%
I93655
 
6.5%
C83111
 
5.8%
S74575
 
5.2%
L73110
 
5.1%
Other values (26)317958
22.1%
Latin 1 Sup
ValueCountFrequency (%)
Ñ592
53.9%
Á217
 
19.7%
Ó144
 
13.1%
É137
 
12.5%
Ì9
 
0.8%

TIPO_DELITO_ARTICULO
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct29
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size123.1 KiB
ARTÍCULO 239
58983 
ARTÍCULO 111
22430 
ARTÍCULO 120
20704 
ARTÍCULO 229
16251 
ARTÍCULO 209
 
1518
Other values (24)
 
4777

Length

Max length14
Median length12
Mean length12.00598413
Min length10

Characters and Unicode

Total characters1496702
Distinct characters22
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowARTÍCULO 111
2nd rowARTÍCULO 111
3rd rowARTÍCULO 111
4th rowARTÍCULO 111
5th rowARTÍCULO 111

Common Values

ValueCountFrequency (%)
ARTÍCULO 23958983
47.3%
ARTÍCULO 11122430
 
18.0%
ARTÍCULO 12020704
 
16.6%
ARTÍCULO 22916251
 
13.0%
ARTÍCULO 2091518
 
1.2%
ARTÍCULO 1031238
 
1.0%
ARTÍCULO 208793
 
0.6%
ARTÍCULO 109568
 
0.5%
ARTÍCULO 205458
 
0.4%
ARTÍCULO 244432
 
0.3%
Other values (19)1288
 
1.0%

Length

2021-09-03T11:08:30.357613image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
artículo124651
49.9%
23958983
23.6%
11122430
 
9.0%
12020704
 
8.3%
22916251
 
6.5%
2091518
 
0.6%
1031238
 
0.5%
208793
 
0.3%
109568
 
0.2%
210504
 
0.2%
Other values (19)2071
 
0.8%

Most occurring characters

ValueCountFrequency (%)
A125048
8.4%
125048
8.4%
R124675
8.3%
O124675
8.3%
T124663
8.3%
Í124651
8.3%
C124651
8.3%
U124651
8.3%
L124651
8.3%
2116622
7.8%
Other values (12)257367
17.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter997701
66.7%
Decimal Number373953
 
25.0%
Space Separator125048
 
8.4%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A125048
12.5%
R124675
12.5%
O124675
12.5%
T124663
12.5%
Í124651
12.5%
C124651
12.5%
U124651
12.5%
L124651
12.5%
N12
 
< 0.1%
E12
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
2116622
31.2%
190637
24.2%
977392
20.7%
360257
16.1%
026301
 
7.0%
4908
 
0.2%
8866
 
0.2%
5462
 
0.1%
6367
 
0.1%
7141
 
< 0.1%
Space Separator
ValueCountFrequency (%)
125048
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin997701
66.7%
Common499001
33.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
A125048
12.5%
R124675
12.5%
O124675
12.5%
T124663
12.5%
Í124651
12.5%
C124651
12.5%
U124651
12.5%
L124651
12.5%
N12
 
< 0.1%
E12
 
< 0.1%
Common
ValueCountFrequency (%)
125048
25.1%
2116622
23.4%
190637
18.2%
977392
15.5%
360257
12.1%
026301
 
5.3%
4908
 
0.2%
8866
 
0.2%
5462
 
0.1%
6367
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1372051
91.7%
Latin 1 Sup124651
 
8.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A125048
9.1%
125048
9.1%
R124675
9.1%
O124675
9.1%
T124663
9.1%
C124651
9.1%
U124651
9.1%
L124651
9.1%
2116622
8.5%
190637
6.6%
Other values (11)166730
12.2%
Latin 1 Sup
ValueCountFrequency (%)
Í124651
100.0%

TIPO_DELITO
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct40
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size123.2 KiB
HURTO A PERSONAS
43204 
LESIONES PERSONALES
22430 
LESIONES CULPOSAS (EN ACCIDENTE DE TRANSITO)
20695 
VIOLENCIA INTRAFAMILIAR
16251 
HURTO A ENTIDADES COMERCIALES
8089 
Other values (35)
13994 

Length

Max length95
Median length19
Mean length23.85142344
Min length9

Characters and Unicode

Total characters2973390
Distinct characters32
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowLESIONES PERSONALES
2nd rowLESIONES PERSONALES
3rd rowLESIONES PERSONALES
4th rowLESIONES PERSONALES
5th rowLESIONES PERSONALES

Common Values

ValueCountFrequency (%)
HURTO A PERSONAS43204
34.7%
LESIONES PERSONALES22430
18.0%
LESIONES CULPOSAS (EN ACCIDENTE DE TRANSITO)20695
16.6%
VIOLENCIA INTRAFAMILIAR16251
 
13.0%
HURTO A ENTIDADES COMERCIALES8089
 
6.5%
HURTO A RESIDENCIAS4336
 
3.5%
HURTO A MOTOCICLETAS3101
 
2.5%
ACTOS SEXUALES CON MENOR DE 14 AÑOS1518
 
1.2%
HOMICIDIO1238
 
1.0%
ACCESO CARNAL ABUSIVO CON MENOR DE 14 AÑOS793
 
0.6%
Other values (30)3008
 
2.4%

Length

2021-09-03T11:08:30.948418image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
a59033
14.1%
hurto58983
14.1%
personas43204
10.3%
lesiones43139
10.3%
de24240
 
5.8%
personales22431
 
5.4%
en21741
 
5.2%
accidente21263
 
5.1%
culposas20704
 
5.0%
transito20695
 
5.0%
Other values (65)82479
19.7%

Most occurring characters

ValueCountFrequency (%)
E334651
11.3%
S322239
10.8%
293249
9.9%
A292202
9.8%
O261997
8.8%
N227609
7.7%
I205469
 
6.9%
R197334
 
6.6%
T158086
 
5.3%
L135813
 
4.6%
Other values (22)544741
18.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter2632829
88.5%
Space Separator293249
 
9.9%
Open Punctuation21304
 
0.7%
Close Punctuation21304
 
0.7%
Decimal Number4704
 
0.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E334651
12.7%
S322239
12.2%
A292202
11.1%
O261997
10.0%
N227609
8.6%
I205469
7.8%
R197334
7.5%
T158086
6.0%
L135813
 
5.2%
C120322
 
4.6%
Other values (16)377107
14.3%
Decimal Number
ValueCountFrequency (%)
12352
50.0%
42343
49.8%
89
 
0.2%
Space Separator
ValueCountFrequency (%)
293249
100.0%
Open Punctuation
ValueCountFrequency (%)
(21304
100.0%
Close Punctuation
ValueCountFrequency (%)
)21304
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2632829
88.5%
Common340561
 
11.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
E334651
12.7%
S322239
12.2%
A292202
11.1%
O261997
10.0%
N227609
8.6%
I205469
7.8%
R197334
7.5%
T158086
6.0%
L135813
 
5.2%
C120322
 
4.6%
Other values (16)377107
14.3%
Common
ValueCountFrequency (%)
293249
86.1%
(21304
 
6.3%
)21304
 
6.3%
12352
 
0.7%
42343
 
0.7%
89
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII2969616
99.9%
Latin 1 Sup3774
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E334651
11.3%
S322239
10.9%
293249
9.9%
A292202
9.8%
O261997
8.8%
N227609
7.7%
I205469
 
6.9%
R197334
 
6.6%
T158086
 
5.3%
L135813
 
4.6%
Other values (18)540967
18.2%
Latin 1 Sup
ValueCountFrequency (%)
Ñ2359
62.5%
Ó758
 
20.1%
Á568
 
15.1%
Í89
 
2.4%

TIPO_CONDUCTA
Categorical

HIGH CARDINALITY

Distinct111
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size126.8 KiB
HURTO A PERSONAS
39101 
LESIONES PERSONALES
21030 
LESIONES CULPOSAS (EN ACCIDENTE DE TRANSITO)
19360 
VIOLENCIA INTRAFAMILIAR
15953 
HURTO A ENTIDADES COMERCIALES
7766 
Other values (106)
21453 

Length

Max length95
Median length19
Mean length23.18374337
Min length5

Characters and Unicode

Total characters2890155
Distinct characters36
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)< 0.1%

Sample

1st rowLESIONES PERSONALES
2nd rowLESIONES PERSONALES
3rd rowLESIONES PERSONALES
4th rowLESIONES PERSONALES
5th rowLESIONES PERSONALES

Common Values

ValueCountFrequency (%)
HURTO A PERSONAS39101
31.4%
LESIONES PERSONALES21030
16.9%
LESIONES CULPOSAS (EN ACCIDENTE DE TRANSITO)19360
15.5%
VIOLENCIA INTRAFAMILIAR15953
12.8%
HURTO A ENTIDADES COMERCIALES7766
 
6.2%
HURTO A RESIDENCIAS4105
 
3.3%
HURTO A MOTOCICLETAS2871
 
2.3%
ACTOS SEXUALES CON MENOR DE 14 AÑOS1436
 
1.2%
ATRACO1344
 
1.1%
RIÑAS1201
 
1.0%
Other values (101)10496
 
8.4%

Length

2021-09-03T11:08:31.921908image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
a54531
13.5%
hurto54521
13.5%
lesiones41259
10.2%
personas39101
9.7%
de23927
 
5.9%
personales21030
 
5.2%
en20983
 
5.2%
accidente20065
 
5.0%
transito19574
 
4.8%
culposas19387
 
4.8%
Other values (165)90524
22.4%

Most occurring characters

ValueCountFrequency (%)
E322332
11.2%
S304923
10.6%
A289221
10.0%
280252
9.7%
O256070
8.9%
N219479
7.6%
I201593
 
7.0%
R193847
 
6.7%
T155009
 
5.4%
L133294
 
4.6%
Other values (26)534135
18.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter2563716
88.7%
Space Separator280252
 
9.7%
Open Punctuation19971
 
0.7%
Close Punctuation19891
 
0.7%
Decimal Number4450
 
0.2%
Other Punctuation1855
 
0.1%
Dash Punctuation20
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E322332
12.6%
S304923
11.9%
A289221
11.3%
O256070
10.0%
N219479
8.6%
I201593
7.9%
R193847
7.6%
T155009
6.0%
L133294
 
5.2%
C118200
 
4.6%
Other values (17)369748
14.4%
Decimal Number
ValueCountFrequency (%)
12225
50.0%
42221
49.9%
84
 
0.1%
Other Punctuation
ValueCountFrequency (%)
/1835
98.9%
.20
 
1.1%
Space Separator
ValueCountFrequency (%)
280252
100.0%
Open Punctuation
ValueCountFrequency (%)
(19971
100.0%
Close Punctuation
ValueCountFrequency (%)
)19891
100.0%
Dash Punctuation
ValueCountFrequency (%)
-20
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2563716
88.7%
Common326439
 
11.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
E322332
12.6%
S304923
11.9%
A289221
11.3%
O256070
10.0%
N219479
8.6%
I201593
7.9%
R193847
7.6%
T155009
6.0%
L133294
 
5.2%
C118200
 
4.6%
Other values (17)369748
14.4%
Common
ValueCountFrequency (%)
280252
85.9%
(19971
 
6.1%
)19891
 
6.1%
12225
 
0.7%
42221
 
0.7%
/1835
 
0.6%
.20
 
< 0.1%
-20
 
< 0.1%
84
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII2885297
99.8%
Latin 1 Sup4858
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E322332
11.2%
S304923
10.6%
A289221
10.0%
280252
9.7%
O256070
8.9%
N219479
7.6%
I201593
 
7.0%
R193847
 
6.7%
T155009
 
5.4%
L133294
 
4.6%
Other values (22)529277
18.3%
Latin 1 Sup
ValueCountFrequency (%)
Ñ3557
73.2%
Ó735
 
15.1%
Á491
 
10.1%
Í75
 
1.5%

TIPO_LESION
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size122.1 KiB
LESIONES NO FATALES
118670 
VIOLENCIA SEXUAL
 
4155
LESIONES FATALES
 
1832
NO REPORTA
 
6

Length

Max length19
Median length19
Mean length18.8554904
Min length10

Characters and Unicode

Total characters2350582
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLESIONES NO FATALES
2nd rowLESIONES NO FATALES
3rd rowLESIONES NO FATALES
4th rowLESIONES NO FATALES
5th rowLESIONES NO FATALES

Common Values

ValueCountFrequency (%)
LESIONES NO FATALES118670
95.2%
VIOLENCIA SEXUAL4155
 
3.3%
LESIONES FATALES1832
 
1.5%
NO REPORTA6
 
< 0.1%

Length

2021-09-03T11:08:32.762683image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-03T11:08:32.965517image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
lesiones120502
32.7%
fatales120502
32.7%
no118676
32.2%
violencia4155
 
1.1%
sexual4155
 
1.1%
reporta6
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
E369822
15.7%
S365661
15.6%
A249320
10.6%
L249314
10.6%
O243339
10.4%
N243333
10.4%
243333
10.4%
I128812
 
5.5%
T120508
 
5.1%
F120502
 
5.1%
Other values (6)16638
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter2107249
89.6%
Space Separator243333
 
10.4%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E369822
17.5%
S365661
17.4%
A249320
11.8%
L249314
11.8%
O243339
11.5%
N243333
11.5%
I128812
 
6.1%
T120508
 
5.7%
F120502
 
5.7%
V4155
 
0.2%
Other values (5)12483
 
0.6%
Space Separator
ValueCountFrequency (%)
243333
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2107249
89.6%
Common243333
 
10.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
E369822
17.5%
S365661
17.4%
A249320
11.8%
L249314
11.8%
O243339
11.5%
N243333
11.5%
I128812
 
6.1%
T120508
 
5.7%
F120502
 
5.7%
V4155
 
0.2%
Other values (5)12483
 
0.6%
Common
ValueCountFrequency (%)
243333
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2350582
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E369822
15.7%
S365661
15.6%
A249320
10.6%
L249314
10.6%
O243339
10.4%
N243333
10.4%
243333
10.4%
I128812
 
5.5%
T120508
 
5.1%
F120502
 
5.1%
Other values (6)16638
 
0.7%

GENERO_VICTIMA
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size122.0 KiB
MASCULINO
61754 
FEMENINO
55061 
NO REPORTA
7848 

Length

Max length10
Median length9
Mean length8.621274957
Min length8

Characters and Unicode

Total characters1074754
Distinct characters15
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMASCULINO
2nd rowMASCULINO
3rd rowMASCULINO
4th rowMASCULINO
5th rowMASCULINO

Common Values

ValueCountFrequency (%)
MASCULINO61754
49.5%
FEMENINO55061
44.2%
NO REPORTA7848
 
6.3%

Length

2021-09-03T11:08:33.471955image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-03T11:08:33.603608image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
masculino61754
46.6%
femenino55061
41.6%
no7848
 
5.9%
reporta7848
 
5.9%

Most occurring characters

ValueCountFrequency (%)
N179724
16.7%
O132511
12.3%
E117970
11.0%
M116815
10.9%
I116815
10.9%
A69602
 
6.5%
S61754
 
5.7%
C61754
 
5.7%
U61754
 
5.7%
L61754
 
5.7%
Other values (5)94301
8.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter1066906
99.3%
Space Separator7848
 
0.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N179724
16.8%
O132511
12.4%
E117970
11.1%
M116815
10.9%
I116815
10.9%
A69602
 
6.5%
S61754
 
5.8%
C61754
 
5.8%
U61754
 
5.8%
L61754
 
5.8%
Other values (4)86453
8.1%
Space Separator
ValueCountFrequency (%)
7848
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1066906
99.3%
Common7848
 
0.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
N179724
16.8%
O132511
12.4%
E117970
11.1%
M116815
10.9%
I116815
10.9%
A69602
 
6.5%
S61754
 
5.8%
C61754
 
5.8%
U61754
 
5.8%
L61754
 
5.8%
Other values (4)86453
8.1%
Common
ValueCountFrequency (%)
7848
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1074754
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N179724
16.7%
O132511
12.3%
E117970
11.0%
M116815
10.9%
I116815
10.9%
A69602
 
6.5%
S61754
 
5.7%
C61754
 
5.7%
U61754
 
5.7%
L61754
 
5.7%
Other values (5)94301
8.8%

EDAD_VICTIMA
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct99
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.03731661
Minimum-1
Maximum100
Zeros28
Zeros (%)< 0.1%
Negative8709
Negative (%)7.0%
Memory size974.1 KiB
2021-09-03T11:08:33.753921image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q121
median29
Q341
95-th percentile61
Maximum100
Range101
Interquartile range (IQR)20

Descriptive statistics

Standard deviation16.65157659
Coefficient of variation (CV)0.5365018116
Kurtosis0.2951144199
Mean31.03731661
Median Absolute Deviation (MAD)9
Skewness0.31940018
Sum3869205
Variance277.2750028
MonotonicityNot monotonic
2021-09-03T11:08:33.961375image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-18709
 
7.0%
254210
 
3.4%
234038
 
3.2%
223883
 
3.1%
263881
 
3.1%
213854
 
3.1%
243828
 
3.1%
203720
 
3.0%
273676
 
2.9%
303603
 
2.9%
Other values (89)81261
65.2%
ValueCountFrequency (%)
-18709
7.0%
028
 
< 0.1%
1106
 
0.1%
2177
 
0.1%
3250
 
0.2%
4254
 
0.2%
5286
 
0.2%
6275
 
0.2%
7279
 
0.2%
8313
 
0.3%
ValueCountFrequency (%)
1001
 
< 0.1%
981
 
< 0.1%
951
 
< 0.1%
945
 
< 0.1%
936
 
< 0.1%
9212
 
< 0.1%
9115
< 0.1%
9022
< 0.1%
8916
< 0.1%
8832
< 0.1%

GRUPO_ETARIO_VICTIMA
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size122.2 KiB
ADULTEZ
58177 
JOVENES
40843 
NO REPORTA
8152 
ADOLESCENCIA
7309 
PERSONA MAYOR
7056 
Other values (2)
 
3126

Length

Max length16
Median length7
Mean length7.942308464
Min length7

Characters and Unicode

Total characters990112
Distinct characters20
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowADULTEZ
2nd rowJOVENES
3rd rowJOVENES
4th rowADULTEZ
5th rowJOVENES

Common Values

ValueCountFrequency (%)
ADULTEZ58177
46.7%
JOVENES40843
32.8%
NO REPORTA8152
 
6.5%
ADOLESCENCIA7309
 
5.9%
PERSONA MAYOR7056
 
5.7%
INFANCIA1750
 
1.4%
PRIMERA INFANCIA1376
 
1.1%

Length

2021-09-03T11:08:34.282424image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-03T11:08:34.398149image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
adultez58177
41.2%
jovenes40843
28.9%
no8152
 
5.8%
reporta8152
 
5.8%
adolescencia7309
 
5.2%
persona7056
 
5.0%
mayor7056
 
5.0%
infancia3126
 
2.2%
primera1376
 
1.0%

Most occurring characters

ValueCountFrequency (%)
E171065
17.3%
A102687
10.4%
O78568
 
7.9%
N69612
 
7.0%
T66329
 
6.7%
D65486
 
6.6%
L65486
 
6.6%
U58177
 
5.9%
Z58177
 
5.9%
S55208
 
5.6%
Other values (10)199317
20.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter973528
98.3%
Space Separator16584
 
1.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E171065
17.6%
A102687
10.5%
O78568
8.1%
N69612
 
7.2%
T66329
 
6.8%
D65486
 
6.7%
L65486
 
6.7%
U58177
 
6.0%
Z58177
 
6.0%
S55208
 
5.7%
Other values (9)182733
18.8%
Space Separator
ValueCountFrequency (%)
16584
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin973528
98.3%
Common16584
 
1.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
E171065
17.6%
A102687
10.5%
O78568
8.1%
N69612
 
7.2%
T66329
 
6.8%
D65486
 
6.7%
L65486
 
6.7%
U58177
 
6.0%
Z58177
 
6.0%
S55208
 
5.7%
Other values (9)182733
18.8%
Common
ValueCountFrequency (%)
16584
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII990112
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E171065
17.3%
A102687
10.4%
O78568
 
7.9%
N69612
 
7.0%
T66329
 
6.7%
D65486
 
6.6%
L65486
 
6.6%
U58177
 
5.9%
Z58177
 
5.9%
S55208
 
5.6%
Other values (10)199317
20.1%

GRUPO_ETARIO_VICTIMA_num
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.198487121
Minimum0
Maximum6
Zeros8152
Zeros (%)6.5%
Negative0
Negative (%)0.0%
Memory size974.1 KiB
2021-09-03T11:08:34.526214image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median5
Q35
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.383507421
Coefficient of variation (CV)0.3295252269
Kurtosis3.199436441
Mean4.198487121
Median Absolute Deviation (MAD)1
Skewness-1.843647666
Sum523396
Variance1.914092784
MonotonicityNot monotonic
2021-09-03T11:08:34.662045image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
558177
46.7%
440843
32.8%
08152
 
6.5%
37309
 
5.9%
67056
 
5.7%
21750
 
1.4%
11376
 
1.1%
ValueCountFrequency (%)
08152
 
6.5%
11376
 
1.1%
21750
 
1.4%
37309
 
5.9%
440843
32.8%
558177
46.7%
67056
 
5.7%
ValueCountFrequency (%)
67056
 
5.7%
558177
46.7%
440843
32.8%
37309
 
5.9%
21750
 
1.4%
11376
 
1.1%
08152
 
6.5%

ESTADO_CIVIL_VICTIMA
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size122.2 KiB
SOLTERO
60871 
CASADO
26607 
UNION LIBRE
25020 
NO REPORTA
8162 
DIVORCIADO
 
1872
Other values (2)
 
2131

Length

Max length11
Median length7
Mean length7.813761902
Min length5

Characters and Unicode

Total characters974087
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUNION LIBRE
2nd rowSOLTERO
3rd rowSOLTERO
4th rowCASADO
5th rowUNION LIBRE

Common Values

ValueCountFrequency (%)
SOLTERO60871
48.8%
CASADO26607
21.3%
UNION LIBRE25020
20.1%
NO REPORTA8162
 
6.5%
DIVORCIADO1872
 
1.5%
VIUDO1420
 
1.1%
SEPARADO711
 
0.6%

Length

2021-09-03T11:08:35.228155image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-03T11:08:35.353854image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
soltero60871
38.6%
casado26607
16.9%
union25020
15.9%
libre25020
15.9%
no8162
 
5.2%
reporta8162
 
5.2%
divorciado1872
 
1.2%
viudo1420
 
0.9%
separado711
 
0.5%

Most occurring characters

ValueCountFrequency (%)
O195568
20.1%
R104798
10.8%
E94764
9.7%
S88189
9.1%
L85891
8.8%
T69033
 
7.1%
A64670
 
6.6%
N58202
 
6.0%
I55204
 
5.7%
33182
 
3.4%
Other values (6)124586
12.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter940905
96.6%
Space Separator33182
 
3.4%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O195568
20.8%
R104798
11.1%
E94764
10.1%
S88189
9.4%
L85891
9.1%
T69033
 
7.3%
A64670
 
6.9%
N58202
 
6.2%
I55204
 
5.9%
D32482
 
3.5%
Other values (5)92104
9.8%
Space Separator
ValueCountFrequency (%)
33182
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin940905
96.6%
Common33182
 
3.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
O195568
20.8%
R104798
11.1%
E94764
10.1%
S88189
9.4%
L85891
9.1%
T69033
 
7.3%
A64670
 
6.9%
N58202
 
6.2%
I55204
 
5.9%
D32482
 
3.5%
Other values (5)92104
9.8%
Common
ValueCountFrequency (%)
33182
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII974087
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
O195568
20.1%
R104798
10.8%
E94764
9.7%
S88189
9.1%
L85891
8.8%
T69033
 
7.1%
A64670
 
6.6%
N58202
 
6.0%
I55204
 
5.7%
33182
 
3.4%
Other values (6)124586
12.8%

MEDIO_TRANSPORTE_VICTIMA
Categorical

HIGH CORRELATION

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size122.5 KiB
A PIE
96332 
CONDUCTOR MOTOCICLETA
16113 
CONDUCTOR VEHICULO
 
5139
PASAJERO BUS
 
1782
BICICLETA
 
1037
Other values (9)
 
4260

Length

Max length21
Median length5
Mean length8.068737316
Min length5

Characters and Unicode

Total characters1005873
Distinct characters20
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowA PIE
2nd rowA PIE
3rd rowA PIE
4th rowA PIE
5th rowA PIE

Common Values

ValueCountFrequency (%)
A PIE96332
77.3%
CONDUCTOR MOTOCICLETA16113
 
12.9%
CONDUCTOR VEHICULO5139
 
4.1%
PASAJERO BUS1782
 
1.4%
BICICLETA1037
 
0.8%
CONDUCTOR TAXI1037
 
0.8%
PASAJERO MOTOCICLETA935
 
0.8%
PASAJERO TAXI757
 
0.6%
NO REPORTA717
 
0.6%
PASAJERO VEHICULO421
 
0.3%
Other values (4)393
 
0.3%

Length

2021-09-03T11:08:35.713016image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
a96332
38.8%
pie96332
38.8%
conductor22562
 
9.1%
motocicleta17048
 
6.9%
vehiculo5560
 
2.2%
pasajero4015
 
1.6%
bus2055
 
0.8%
taxi1794
 
0.7%
bicicleta1037
 
0.4%
no717
 
0.3%
Other values (4)837
 
0.3%

Most occurring characters

ValueCountFrequency (%)
A124963
12.4%
E124830
12.4%
123626
12.3%
I122808
12.2%
P101064
10.0%
O90349
9.0%
C86855
8.6%
T60323
6.0%
U30177
 
3.0%
R28131
 
2.8%
Other values (10)112747
11.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter882247
87.7%
Space Separator123626
 
12.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A124963
14.2%
E124830
14.1%
I122808
13.9%
P101064
11.5%
O90349
10.2%
C86855
9.8%
T60323
6.8%
U30177
 
3.4%
R28131
 
3.2%
L23645
 
2.7%
Other values (9)89102
10.1%
Space Separator
ValueCountFrequency (%)
123626
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin882247
87.7%
Common123626
 
12.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
A124963
14.2%
E124830
14.1%
I122808
13.9%
P101064
11.5%
O90349
10.2%
C86855
9.8%
T60323
6.8%
U30177
 
3.4%
R28131
 
3.2%
L23645
 
2.7%
Other values (9)89102
10.1%
Common
ValueCountFrequency (%)
123626
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1005873
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A124963
12.4%
E124830
12.4%
123626
12.3%
I122808
12.2%
P101064
10.0%
O90349
9.0%
C86855
8.6%
T60323
6.0%
U30177
 
3.0%
R28131
 
2.8%
Other values (10)112747
11.2%

MEDIO_TRANSPORTE_VICTIMARIO
Categorical

HIGH CORRELATION

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size122.5 KiB
A PIE
85850 
CONDUCTOR MOTOCICLETA
14258 
CONDUCTOR VEHICULO
12688 
PASAJERO MOTOCICLETA
 
6094
CONDUCTOR TAXI
 
2868
Other values (8)
 
2905

Length

Max length21
Median length5
Mean length9.256692042
Min length5

Characters and Unicode

Total characters1153967
Distinct characters20
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowA PIE
2nd rowA PIE
3rd rowA PIE
4th rowA PIE
5th rowA PIE

Common Values

ValueCountFrequency (%)
A PIE85850
68.9%
CONDUCTOR MOTOCICLETA14258
 
11.4%
CONDUCTOR VEHICULO12688
 
10.2%
PASAJERO MOTOCICLETA6094
 
4.9%
CONDUCTOR TAXI2868
 
2.3%
PASAJERO BUS1454
 
1.2%
PASAJERO TAXI523
 
0.4%
BICICLETA333
 
0.3%
NO REPORTA270
 
0.2%
PASAJERO VEHICULO152
 
0.1%
Other values (3)173
 
0.1%

Length

2021-09-03T11:08:36.108678image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
a85850
34.5%
pie85850
34.5%
conductor29886
 
12.0%
motocicleta20352
 
8.2%
vehiculo12840
 
5.2%
pasajero8323
 
3.3%
taxi3391
 
1.4%
bus1526
 
0.6%
bicicleta333
 
0.1%
no270
 
0.1%
Other values (4)372
 
0.1%

Most occurring characters

ValueCountFrequency (%)
E128071
11.1%
A126845
11.0%
124330
10.8%
I123100
10.7%
O122280
10.6%
C113982
9.9%
P94444
8.2%
T74686
6.5%
U44253
 
3.8%
R38851
 
3.4%
Other values (10)163125
14.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter1029637
89.2%
Space Separator124330
 
10.8%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E128071
12.4%
A126845
12.3%
I123100
12.0%
O122280
11.9%
C113982
11.1%
P94444
9.2%
T74686
7.3%
U44253
 
4.3%
R38851
 
3.8%
L33526
 
3.3%
Other values (9)129599
12.6%
Space Separator
ValueCountFrequency (%)
124330
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1029637
89.2%
Common124330
 
10.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
E128071
12.4%
A126845
12.3%
I123100
12.0%
O122280
11.9%
C113982
11.1%
P94444
9.2%
T74686
7.3%
U44253
 
4.3%
R38851
 
3.8%
L33526
 
3.3%
Other values (9)129599
12.6%
Common
ValueCountFrequency (%)
124330
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1153967
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E128071
11.1%
A126845
11.0%
124330
10.8%
I123100
10.7%
O122280
10.6%
C113982
9.9%
P94444
8.2%
T74686
6.5%
U44253
 
3.8%
R38851
 
3.4%
Other values (10)163125
14.1%

TIPO_ARMA
Categorical

HIGH CORRELATION

Distinct36
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size123.2 KiB
CONTUNDENTES
35333 
SIN EMPLEO DE ARMAS
33671 
ARMA BLANCA/CORTOPUNZANTE
20301 
VEHICULO
13132 
ARMA DE FUEGO
7779 
Other values (31)
14447 

Length

Max length34
Median length13
Mean length15.11082679
Min length4

Characters and Unicode

Total characters1883761
Distinct characters27
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowARMA BLANCA/CORTOPUNZANTE
2nd rowARMA BLANCA/CORTOPUNZANTE
3rd rowARMA BLANCA/CORTOPUNZANTE
4th rowARMA BLANCA/CORTOPUNZANTE
5th rowARMA BLANCA/CORTOPUNZANTE

Common Values

ValueCountFrequency (%)
CONTUNDENTES35333
28.3%
SIN EMPLEO DE ARMAS33671
27.0%
ARMA BLANCA/CORTOPUNZANTE20301
16.3%
VEHICULO13132
 
10.5%
ARMA DE FUEGO7779
 
6.2%
MOTO7739
 
6.2%
LLAVE MAESTRA2368
 
1.9%
NO REPORTA2107
 
1.7%
PALANCAS906
 
0.7%
ESCOPOLAMINA609
 
0.5%
Other values (26)718
 
0.6%

Length

2021-09-03T11:08:36.517534image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
de41460
15.6%
contundentes35333
13.3%
sin33671
12.6%
empleo33671
12.6%
armas33671
12.6%
arma28080
10.5%
blanca/cortopunzante20301
7.6%
vehiculo13132
 
4.9%
fuego7779
 
2.9%
moto7739
 
2.9%
Other values (47)11580
 
4.3%

Most occurring characters

ValueCountFrequency (%)
E229081
12.2%
N204546
10.9%
A199106
10.6%
O152020
 
8.1%
141754
 
7.5%
T124015
 
6.6%
S106849
 
5.7%
M106480
 
5.7%
C91229
 
4.8%
R89010
 
4.7%
Other values (17)439671
23.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter1721679
91.4%
Space Separator141754
 
7.5%
Other Punctuation20326
 
1.1%
Open Punctuation1
 
< 0.1%
Close Punctuation1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E229081
13.3%
N204546
11.9%
A199106
11.6%
O152020
8.8%
T124015
 
7.2%
S106849
 
6.2%
M106480
 
6.2%
C91229
 
5.3%
R89010
 
5.2%
D77291
 
4.5%
Other values (13)342052
19.9%
Space Separator
ValueCountFrequency (%)
141754
100.0%
Other Punctuation
ValueCountFrequency (%)
/20326
100.0%
Open Punctuation
ValueCountFrequency (%)
(1
100.0%
Close Punctuation
ValueCountFrequency (%)
)1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1721679
91.4%
Common162082
 
8.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
E229081
13.3%
N204546
11.9%
A199106
11.6%
O152020
8.8%
T124015
 
7.2%
S106849
 
6.2%
M106480
 
6.2%
C91229
 
5.3%
R89010
 
5.2%
D77291
 
4.5%
Other values (13)342052
19.9%
Common
ValueCountFrequency (%)
141754
87.5%
/20326
 
12.5%
(1
 
< 0.1%
)1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1883761
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E229081
12.2%
N204546
10.9%
A199106
10.6%
O152020
 
8.1%
141754
 
7.5%
T124015
 
6.6%
S106849
 
5.7%
M106480
 
5.7%
C91229
 
4.8%
R89010
 
4.7%
Other values (17)439671
23.3%

DISTANCIA_ESTACION_POLICIA_CERCANA
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
SKEWED

Distinct69813
Distinct (%)57.9%
Missing4121
Missing (%)3.3%
Infinite0
Infinite (%)0.0%
Mean0.5462961437
Minimum0.001486
Maximum300.340824
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size974.1 KiB
2021-09-03T11:08:36.702724image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.001486
5-th percentile0.1079592
Q10.252911
median0.400576
Q30.620965
95-th percentile1.5417977
Maximum300.340824
Range300.339338
Interquartile range (IQR)0.368054

Descriptive statistics

Standard deviation1.013872215
Coefficient of variation (CV)1.855902201
Kurtosis63421.97897
Mean0.5462961437
Median Absolute Deviation (MAD)0.172421
Skewness214.9790344
Sum65851.62976
Variance1.027936869
MonotonicityNot monotonic
2021-09-03T11:08:36.890229image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.1989071799
 
1.4%
1.21121375
 
1.1%
0.2701291164
 
0.9%
0.4190481073
 
0.9%
0.1956891004
 
0.8%
1.016831842
 
0.7%
0.155217804
 
0.6%
0.483664768
 
0.6%
0.210898761
 
0.6%
0.547459703
 
0.6%
Other values (69803)110249
88.4%
(Missing)4121
 
3.3%
ValueCountFrequency (%)
0.0014861
< 0.1%
0.0020241
< 0.1%
0.0024191
< 0.1%
0.0025621
< 0.1%
0.0031351
< 0.1%
0.0033341
< 0.1%
0.0037031
< 0.1%
0.0040261
< 0.1%
0.0040321
< 0.1%
0.0041481
< 0.1%
ValueCountFrequency (%)
300.3408241
< 0.1%
11.5075791
< 0.1%
9.7259231
< 0.1%
9.6843241
< 0.1%
8.1086641
< 0.1%
8.1009491
< 0.1%
7.9896061
< 0.1%
7.3229761
< 0.1%
6.8069461
< 0.1%
6.5283461
< 0.1%

ESTACION_POLICIA_CERCANA
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct36
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size123.2 KiB
CAI CAFÉ MADRID
11816 
CAI LA ESPERANZA
 
7269
CAI SAN FRANCISCO
 
6702
CAI LA CONCORDIA
 
6111
CAI VIADUCTO
 
6002
Other values (31)
86763 

Length

Max length34
Median length15
Mean length15.83666365
Min length7

Characters and Unicode

Total characters1974246
Distinct characters30
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCAI CAFÉ MADRID
2nd rowCAI CENTENARIO
3rd rowCAI CENTENARIO
4th rowCAI CAFÉ MADRID
5th rowCAI CAFÉ MADRID

Common Values

ValueCountFrequency (%)
CAI CAFÉ MADRID11816
 
9.5%
CAI LA ESPERANZA7269
 
5.8%
CAI SAN FRANCISCO6702
 
5.4%
CAI LA CONCORDIA6111
 
4.9%
CAI VIADUCTO6002
 
4.8%
CAI KENNEDY5634
 
4.5%
CAI INEM5066
 
4.1%
CAI SAN ALONSO4590
 
3.7%
CAI CENTENARIO4321
 
3.5%
NO REPORTA4121
 
3.3%
Other values (26)63031
50.6%

Length

2021-09-03T11:08:37.273227image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
cai100619
28.7%
la20312
 
5.8%
de18007
 
5.1%
san13503
 
3.9%
madrid11816
 
3.4%
café11816
 
3.4%
estación9976
 
2.8%
policía9976
 
2.8%
policia9947
 
2.8%
santander8358
 
2.4%
Other values (42)135830
38.8%

Most occurring characters

ValueCountFrequency (%)
A303930
15.4%
225497
11.4%
I218378
11.1%
C210764
10.7%
O145219
7.4%
N124845
 
6.3%
E115016
 
5.8%
R114179
 
5.8%
S89559
 
4.5%
D77397
 
3.9%
Other values (20)349462
17.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter1741384
88.2%
Space Separator225497
 
11.4%
Decimal Number3998
 
0.2%
Dash Punctuation3367
 
0.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A303930
17.5%
I218378
12.5%
C210764
12.1%
O145219
8.3%
N124845
7.2%
E115016
 
6.6%
R114179
 
6.6%
S89559
 
5.1%
D77397
 
4.4%
T71218
 
4.1%
Other values (16)270879
15.6%
Decimal Number
ValueCountFrequency (%)
41999
50.0%
21999
50.0%
Space Separator
ValueCountFrequency (%)
225497
100.0%
Dash Punctuation
ValueCountFrequency (%)
-3367
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1741384
88.2%
Common232862
 
11.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
A303930
17.5%
I218378
12.5%
C210764
12.1%
O145219
8.3%
N124845
7.2%
E115016
 
6.6%
R114179
 
6.6%
S89559
 
5.1%
D77397
 
4.4%
T71218
 
4.1%
Other values (16)270879
15.6%
Common
ValueCountFrequency (%)
225497
96.8%
-3367
 
1.4%
41999
 
0.9%
21999
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII1940267
98.3%
Latin 1 Sup33979
 
1.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A303930
15.7%
225497
11.6%
I218378
11.3%
C210764
10.9%
O145219
7.5%
N124845
 
6.4%
E115016
 
5.9%
R114179
 
5.9%
S89559
 
4.6%
D77397
 
4.0%
Other values (16)315483
16.3%
Latin 1 Sup
ValueCountFrequency (%)
É11816
34.8%
Ó9976
29.4%
Í9976
29.4%
Ñ2211
 
6.5%

Interactions

2021-09-03T11:07:50.912218image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:07:51.122630image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:07:51.306437image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:07:51.533207image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:07:51.693811image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:07:51.866587image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:07:52.043099image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:07:52.228101image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:07:52.410569image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:07:52.591196image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:07:52.767803image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:07:52.962498image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:07:53.209872image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:07:53.375406image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:07:53.544559image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:07:53.736053image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:07:53.930565image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:07:54.111048image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:07:54.335295image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:07:54.593718image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:07:54.830091image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:07:55.041521image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:07:55.247937image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:07:55.461443image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:07:55.646914image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:07:56.071227image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:07:56.295626image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:07:56.583270image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:07:56.855577image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:07:57.258054image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:07:57.556676image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:07:57.847444image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:07:58.071933image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:07:58.301489image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:07:58.512955image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:07:58.721836image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:07:58.921311image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:07:59.166580image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:07:59.420223image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:07:59.675898image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:07:59.920325image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:08:00.134740image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:08:00.340155image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:08:00.535667image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:08:00.738204image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:08:00.943357image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:08:01.160810image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:08:01.384308image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:08:01.584375image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:08:01.798662image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:08:02.012199image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:08:02.254618image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:08:02.508895image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:08:02.745267image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:08:03.165210image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:08:03.355117image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:08:03.569021image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:08:03.753860image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:08:03.941448image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:08:04.142703image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:08:04.323845image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:08:04.531520image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:08:04.713939image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:08:04.922782image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:08:05.143775image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:08:05.409169image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:08:05.626619image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:08:05.832039image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:08:06.029511image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:08:06.221369image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:08:06.437175image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:08:06.668878image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:08:06.892347image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:08:07.144464image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:08:07.401462image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:08:07.663759image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:08:07.916978image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:08:08.181780image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:08:08.418188image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:08:08.687927image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:08:08.947826image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:08:09.179207image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:08:09.426869image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:08:09.856755image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:08:10.085153image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:08:10.342961image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:08:10.606764image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:08:10.865072image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:08:11.128369image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:08:11.389229image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:08:11.662997image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:08:11.909328image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:08:12.179606image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:08:12.447927image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:08:12.719216image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:08:12.995492image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:08:13.231410image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:08:13.473825image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:08:13.704209image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:08:13.931639image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:08:14.133097image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:08:14.346005image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:08:14.537920image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:08:14.722603image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:08:14.908265image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:08:15.101741image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:08:15.300211image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:08:15.513676image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:08:15.764558image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:08:15.977499image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:08:16.219526image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:08:16.413423image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:08:16.769048image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:08:16.944615image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:08:17.134838image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:08:17.325514image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:08:17.532902image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:08:17.760501image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:08:17.989887image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:08:18.190425image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-09-03T11:08:18.459266image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2021-09-03T11:08:37.433853image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-09-03T11:08:37.729439image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-09-03T11:08:38.040640image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-09-03T11:08:38.351774image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-09-03T11:08:38.805747image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-09-03T11:08:19.064845image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2021-09-03T11:08:20.501236image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-09-03T11:08:21.475761image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-09-03T11:08:21.862565image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

CRIMEN_IDFECHAAÑOMESMES_numDIADIA_SEMANADIA_SEMANA_numLATITUDLONGITUDZONACOMUNACOMUNA_numBARRIOUNIDAD_ESPACIALTIPO_DELITO_ARTICULOTIPO_DELITOTIPO_CONDUCTATIPO_LESIONGENERO_VICTIMAEDAD_VICTIMAGRUPO_ETARIO_VICTIMAGRUPO_ETARIO_VICTIMA_numESTADO_CIVIL_VICTIMAMEDIO_TRANSPORTE_VICTIMAMEDIO_TRANSPORTE_VICTIMARIOTIPO_ARMADISTANCIA_ESTACION_POLICIA_CERCANAESTACION_POLICIA_CERCANA
012010-01-012010ENERO11VIERNES57.170557-73.135108URBANAMORRORICO14BUENOS AIRESCORREGIMIENTO 1ARTÍCULO 111LESIONES PERSONALESLESIONES PERSONALESLESIONES NO FATALESMASCULINO30ADULTEZ5UNION LIBREA PIEA PIEARMA BLANCA/CORTOPUNZANTE1.211200CAI CAFÉ MADRID
122010-01-012010ENERO11VIERNES57.120645-73.126050URBANAGARCÍA ROVIRA5CAMPO HERMOSOCENTROARTÍCULO 111LESIONES PERSONALESLESIONES PERSONALESLESIONES NO FATALESMASCULINO21JOVENES4SOLTEROA PIEA PIEARMA BLANCA/CORTOPUNZANTE0.195689CAI CENTENARIO
232010-01-012010ENERO11VIERNES57.120645-73.126050URBANAGARCÍA ROVIRA5CAMPO HERMOSOCENTROARTÍCULO 111LESIONES PERSONALESLESIONES PERSONALESLESIONES NO FATALESMASCULINO23JOVENES4SOLTEROA PIEA PIEARMA BLANCA/CORTOPUNZANTE0.195689CAI CENTENARIO
342010-01-012010ENERO11VIERNES57.151359-73.145705URBANASAN FRANCISCO3COMUNEROSVILLAS DE SAN IGNACIOARTÍCULO 111LESIONES PERSONALESLESIONES PERSONALESLESIONES NO FATALESMASCULINO36ADULTEZ5CASADOA PIEA PIEARMA BLANCA/CORTOPUNZANTE1.230792CAI CAFÉ MADRID
452010-01-012010ENERO11VIERNES57.170557-73.135108URBANAOCCIDENTAL4GIRARDOTCORREGIMIENTO 1ARTÍCULO 111LESIONES PERSONALESLESIONES PERSONALESLESIONES NO FATALESMASCULINO20JOVENES4UNION LIBREA PIEA PIEARMA BLANCA/CORTOPUNZANTE1.211200CAI CAFÉ MADRID
562010-01-012010ENERO11VIERNES57.170557-73.135108URBANAOCCIDENTAL4GIRARDOTCORREGIMIENTO 1ARTÍCULO 111LESIONES PERSONALESLESIONES PERSONALESLESIONES NO FATALESMASCULINO20JOVENES4UNION LIBREA PIEA PIEARMA BLANCA/CORTOPUNZANTE1.211200CAI CAFÉ MADRID
672010-01-012010ENERO11VIERNES57.187455-73.131727URBANANOR ORIENTAL2LOS ANGELESVILLA LUZARTÍCULO 111LESIONES PERSONALESLESIONES PERSONALESLESIONES NO FATALESMASCULINO28JOVENES4CASADOA PIEA PIEARMA BLANCA/CORTOPUNZANTE3.049477CAI CAFÉ MADRID
782010-01-012010ENERO11VIERNES57.156554-73.140753URBANAOCCIDENTAL4NARIÑOVILLAS DE SAN IGNACIOARTÍCULO 111LESIONES PERSONALESLESIONES PERSONALESLESIONES NO FATALESMASCULINO42ADULTEZ5SOLTEROA PIEA PIEARMA BLANCA/CORTOPUNZANTE0.572691CAI CAFÉ MADRID
892010-01-012010ENERO11VIERNES57.120001-73.116084URBANAPROVENZA10PROVENZABOLIVARARTÍCULO 111LESIONES PERSONALESLESIONES PERSONALESLESIONES NO FATALESMASCULINO22JOVENES4SOLTEROA PIEA PIEARMA BLANCA/CORTOPUNZANTE0.466600POLICIA SANTANDER SIC-SIP
9102010-01-012010ENERO11VIERNES57.161314-73.139957URBANACABECERA DEL LLANO12SOTOMAYORCAFE MADRIDARTÍCULO 111LESIONES PERSONALESLESIONES PERSONALESLESIONES NO FATALESMASCULINO20JOVENES4SOLTEROA PIEA PIEARMA BLANCA/CORTOPUNZANTE0.177544CAI CAFÉ MADRID

Last rows

CRIMEN_IDFECHAAÑOMESMES_numDIADIA_SEMANADIA_SEMANA_numLATITUDLONGITUDZONACOMUNACOMUNA_numBARRIOUNIDAD_ESPACIALTIPO_DELITO_ARTICULOTIPO_DELITOTIPO_CONDUCTATIPO_LESIONGENERO_VICTIMAEDAD_VICTIMAGRUPO_ETARIO_VICTIMAGRUPO_ETARIO_VICTIMA_numESTADO_CIVIL_VICTIMAMEDIO_TRANSPORTE_VICTIMAMEDIO_TRANSPORTE_VICTIMARIOTIPO_ARMADISTANCIA_ESTACION_POLICIA_CERCANAESTACION_POLICIA_CERCANA
1246531246542021-02-262021FEBRERO226VIERNES57.133930-73.126930URBANASAN FRANCISCO3SAN FRANCISCOMUTUALIDADARTÍCULO 239HURTO A MOTOCICLETASNO REPORTALESIONES NO FATALESMASCULINO41ADULTEZ5CASADOA PIEA PIESIN EMPLEO DE ARMAS0.413128CAI SAN FRANCISCO
1246541246552021-02-272021FEBRERO227SÁBADO67.091319-73.117514URBANAPROVENZA10SAN LUISSAN LUISARTÍCULO 239HURTO A MOTOCICLETASNO REPORTALESIONES NO FATALESMASCULINO20JOVENES4SOLTEROA PIEA PIESIN EMPLEO DE ARMAS0.661552CAI SUR
1246551246562021-02-102021FEBRERO210MIÉRCOLES37.153306-73.136682URBANANORTE1TEJAR NORTE (SECTOR II )KENNEDYARTÍCULO 239HURTO A MOTOCICLETASNO REPORTALESIONES NO FATALESMASCULINO21JOVENES4SOLTEROA PIEA PIESIN EMPLEO DE ARMAS0.354619CAI KENNEDY
1246561246572021-02-092021FEBRERO29MARTES27.098716-73.132870URBANAMUTIS17URB. BRISAS DEL MUTISMUTISARTÍCULO 239HURTO A MOTOCICLETASNO REPORTALESIONES NO FATALESMASCULINO27JOVENES4SOLTEROA PIEA PIESIN EMPLEO DE ARMAS0.206512CAI MUTIS
1246571246582021-02-112021FEBRERO211JUEVES47.138767-73.072906RURALCORREGIMIENTO 30VDA. RETIRO CHIQUITOCORREGIMIENTO 3ARTÍCULO 239HURTO A MOTOCICLETASNO REPORTALESIONES NO FATALESFEMENINO38ADULTEZ5SOLTEROA PIEA PIESIN EMPLEO DE ARMAS3.511822CAI MORRORICO
1246581246592021-02-202021FEBRERO220SÁBADO67.092583-73.106933URBANASUR11VILLA INESDIAMANTE IARTÍCULO 239HURTO A MOTOCICLETASNO REPORTALESIONES NO FATALESMASCULINO33ADULTEZ5SOLTEROA PIEA PIESIN EMPLEO DE ARMAS0.592530CAI VIADUCTO
1246591246602021-02-072021FEBRERO27DOMINGO77.148965-73.115512RURALCORREGIMIENTO 10VRDA ABEJASCORREGIMIENTO 3ARTÍCULO 239HURTO A MOTOCICLETASNO REPORTALESIONES NO FATALESMASCULINO35ADULTEZ5SOLTEROA PIEA PIESIN EMPLEO DE ARMAS1.247678CAI LA ESPERANZA
1246601246612021-02-042021FEBRERO24JUEVES47.094033-73.132868URBANAMUTIS17MUTISMONTERREDONDOARTÍCULO 239HURTO A AUTOMOTORESNO REPORTALESIONES NO FATALESFEMENINO29ADULTEZ5UNION LIBREA PIEA PIESIN EMPLEO DE ARMAS0.693896CAI MUTIS
1246611246622021-02-032021FEBRERO23MIÉRCOLES37.078868-73.117173URBANASUR11URB. BRISAS DE PROVENZABRISAS DE PROVENZAARTÍCULO 239HURTO A AUTOMOTORESNO REPORTALESIONES NO FATALESMASCULINO34ADULTEZ5UNION LIBREA PIEA PIESIN EMPLEO DE ARMAS0.718624CAI INEM
1246621246632021-02-052021FEBRERO25VIERNES57.097263-73.125917URBANALA CIUDADELA7URB. CIUDAD BOLIVARREAL DE MINASARTÍCULO 239HURTO A AUTOMOTORESNO REPORTALESIONES NO FATALESMASCULINO29ADULTEZ5SOLTEROA PIEA PIESIN EMPLEO DE ARMAS0.198304CAI REAL DE MINAS